The standard measure of kurtosis is based on a scaled version of the fourth moment of the data or population. Therefore, the measure of kurtosis is related to the tails of the distribution, not its peak. Sometimes, Measure of Kurtosis is characterized as a measure of peakedness is mistaken.
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What is kurtosis and how it is measured?
Kurtosis is a statistical measure used to describe the degree to which scores cluster in the tails or the peak of a frequency distribution. The peak is the tallest part of the distribution, and the tails are the ends of the distribution. There are three types of kurtosis: mesokurtic, leptokurtic, and platykurtic.
Why do we measure kurtosis?
Kurtosis is a statistical measure that defines how heavily the tails of a distribution differ from the tails of a normal distribution. In other words, kurtosis identifies whether the tails of a given distribution contain extreme values.In finance, kurtosis is used as a measure of financial risk. Learn risk analysis.
What is a normal kurtosis value?
3
2.3.
A standard normal distribution has kurtosis of 3 and is recognized as mesokurtic. An increased kurtosis (>3) can be visualized as a thin “bell” with a high peak whereas a decreased kurtosis corresponds to a broadening of the peak and “thickening” of the tails.
What does a kurtosis of 3 mean?
If the kurtosis is greater than 3, then the dataset has heavier tails than a normal distribution (more in the tails). If the kurtosis is less than 3, then the dataset has lighter tails than a normal distribution (less in the tails).
How do you explain kurtosis?
Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers. Data sets with low kurtosis tend to have light tails, or lack of outliers. A uniform distribution would be the extreme case.
How do you describe kurtosis?
Kurtosis is all about the tails of the distribution — not the peakedness or flatness. It is used to describe the extreme values in one versus the other tail. It is actually the measure of outliers present in the distribution . High kurtosis in a data set is an indicator that data has heavy tails or outliers.
What is good skewness and kurtosis?
The values for asymmetry and kurtosis between -2 and +2 are considered acceptable in order to prove normal univariate distribution (George & Mallery, 2010). Hair et al. (2010) and Bryne (2010) argued that data is considered to be normal if skewness is between ‐2 to +2 and kurtosis is between ‐7 to +7.
What is meant by kurtosis in statistics?
Kurtosis is a measure of the combined weight of a distribution’s tails relative to the center of the distribution. When a set of approximately normal data is graphed via a histogram, it shows a bell peak and most data within three standard deviations (plus or minus) of the mean.
Is kurtosis a measure of dispersion?
The kurtosis can now be seen as a measure of the dispersion of Z2 around its expectation. Alternatively it can be seen to be a measure of the dispersion of Z around +1 and −1. κ attains its minimal value in a symmetric two-point distribution.
How do you find the kurtosis of a normal distribution?
The normal distribution has skewness equal to zero. The kurtosis of a probability distribution of a random variable x is defined as the ratio of the fourth moment μ4 to the square of the variance σ4, i.e., μ 4 σ 4 = E { ( x − E { x } σ ) 4 } E { x − E { x } } 4 σ 4 . κ = μ 4 σ 4 −3 .
What does negative kurtosis tell us?
A distribution with a negative kurtosis value indicates that the distribution has lighter tails than the normal distribution. For example, data that follow a beta distribution with first and second shape parameters equal to 2 have a negative kurtosis value.
Is Excel Kurt excess kurtosis?
Excel’s KURT( ) function computes the excess kurtosis, not the kurtosis, and so it will always return a value 3 less than the StatTools value.
How do you calculate skewness and kurtosis?
1. Formula & Examples
- Sample Standard deviation S=√∑(x-ˉx)2n-1.
- Skewness =∑(x-ˉx)3(n-1)⋅S3.
- Kurtosis =∑(x-ˉx)4(n-1)⋅S4.
How do you calculate kurtosis in SPSS?
How to Calculate Skewness and Kurtosis in SPSS
- Click on Analyze -> Descriptive Statistics -> Descriptives.
- Drag and drop the variable for which you wish to calculate skewness and kurtosis into the box on the right.
- Click on Options, and select Skewness and Kurtosis.
- Click on Continue, and then OK.
How do you interpret kurtosis in SPSS?
Kurtosis: a measure of the “peakedness” or “flatness” of a distribution. A kurtosis value near zero indicates a shape close to normal. A negative value indicates a distribution which is more peaked than normal, and a positive kurtosis indicates a shape flatter than normal.